In an IRL framework, the learner infers a reward function for a given task from expert demonstrations of the task. The underlying assumption is that the reward function is a parsimonious and portable representation of the task, which can be transferred and generalized to agents with different embodiments. Thus, IRL implicitly resolves the correspondence problem but has the disadvantage of being computationally expensive and requires reinforcement learning in an inner loop. IRL has been implemented mostly in a model-based approach for tasks, such as learning to drive a car in a simulator [1]} and path planning [2]}, [3]}, [4]}. A few model-free IRL algorithms have been proposed and used to learn policies of robot motion [5]}, [6]}, [7]}.
